Continuous-variable quantum approximate optimization on a programmable photonic quantum processor

• URL: http://arxiv.org/abs/2206.07214v2
• Date: Thu, 5 Oct 2023 04:36:18 GMT
• Title: Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
• Authors: Yutaro Enomoto, Keitaro Anai, Kenta Udagawa, Shuntaro Takeda
• Abstract summary: Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on noisy quantum devices. We develop an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.
• Score: 0.9558392439655016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly-distributed wavefunction to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.

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